4 Ways Returns Data Can Improve Customer Lifetime Value (CLV) in Ecommerce
In the dynamic and competitive world of ecommerce, businesses constantly strive to optimize their operations and maximize customer satisfaction and improve customer lifetime value (CLV) in ecommerce.
One often-overlooked treasure trove of valuable information lies within returns data. The customer data generated through returns management software can offer deep insights into customer behavior, preferences, pain points, and opportunities for improvement.
Returns data empowers ecommerce merchants with making business decisions that could help to improve customer lifetime value (CLV). Patterns and trends discovered in returns data can help improve the UX of your Shopify store. This can lead to business growth, higher customer satisfaction, and the development of new marketing strategies.
This article examines the four best ways to use ecommerce returns data to improve customer lifetime value. Doing so can make your ecommerce business more profitable and give customers a better experience.
1. Enhance the Returns Process
The first step in using returns to improve customer lifetime value (CLV) is to optimize the returns process itself.
The returns process is a critical touchpoint in the ecommerce customer journey. Shoppers are changing their behaviors to accommodate the ecommerce environment. As a result, returns have become normalized and expected by customers. They now anticipate it as part of their buying experience.
Shopping habits have changed. In the past, people bought items to keep. Now, shoppers make purchase decisions with the intention of returning them after use.
Understanding this change in shopper behavior helps highlight the importance of optimizing the returns process. Shoppers expect to return products. They also expect the returns process to be simple and easy.
Optimizing the returns process improves CLV because it means having a convenient and hassle-free post-purchase process. Shoppers will be satisfied with their experience in your online shop. They will want to come back, even if they have to make a return.
It’s also important that merchants provide clear instructions on how to initiate an RMA, offer prepaid return labels, and streamline the refund process. Creating a smooth returns process with these three steps helps reduce customer frustration. It also builds trust and loyalty, leading to a positive user experience.
2. Optimize Your Inventory Management
Returns data can also be utilized to optimize inventory management and purchasing decisions. Analyzing returns patterns helps identify products with high return rates or low demand. This information enables ecommerce merchants to adjust inventory levels to fit customer demand.
Ensuring proper inventory levels for each product reduces the chance of customers constantly running into out-of-stock products. This helps reduce the customer frustration caused by not having the products they want available.
On the flip side, having large inventory levels for products with low demand comes at a disadvantage to ecommerce merchants. Inaccurate inventory levels can be costly. High inventory holding costs, misused budgets, and lack of capital to purchase needed products can all lead to problems.
Returns data helps Shopify merchants identify trends or purchase patterns that allow them to predict inventory needs more accurately. With this information, merchants can make data-driven purchasing decisions that save them money and keep customers happy.
3. Improve Your Shopify Store’s User Experience
User experience (UX) lies at the heart of every successful Shopify store. It encompasses the entire customer journey, from the moment they land on your site to the post-purchase experience. As such, without a positive UX, it’s hard to encourage customers to keep coming back for more.
To truly optimize the UX and create a seamless shopping experience, businesses must tap into the power of returns data. Return reasons and customer feedback hold valuable insights into pain points and areas of friction in the customer journey.
Businesses can identify customer pain points, bottlenecks, and sources of friction. They can then use this information to make targeted improvements and optimizations. This will create a better customer experience and improve customer lifetime value (CLV).
Returns data can be used to improve a Shopify store’s site navigation and search functionality. Analyzing returns data gives ecommerce merchants insights into products that are frequently returned or generate customer frustration.
Shopper confusion can also be a factor that’s hurting the overall store UX. It could be a complicated checkout process, unclear product descriptions, or difficulty in finding relevant information.
Anything that complicates the checkout process can impede a seamless user experience on your site. Keep the process as simple and clear as possible to ensure a positive experience.
Armed with this knowledge, you can make informed decisions to reorganize your site’s navigation, improve categories, and enhance search filters. Optimizations make it simpler for customers to locate products that fit their preferences and needs. This creates a more user-friendly and intuitive shopping experience.
Improve Your Store’s Product Pages
A crucial part of your Shopify store is your product pages and the user experience (UX). Shoppers cannot physically touch and feel products when shopping online. Product pages act as a substitute to help them understand what they are buying.
Having unclear or misleading product pages leads to heavy amounts of customer frustration. Customers may be discouraged from making repeat online purchases if they think the product pages are not helpful. This could be detrimental to businesses and hurt your overall CLV if your store’s pages have a high bounce rate.
Luckily, returns data provides ecommerce merchants with valuable insights into the common reasons for returns. When products are repeatedly returned for the same reasons, this may be a sign that your product pages require updating. Common causes of returns include incorrect sizing information and misleading product images.
There are a number of updates ecommerce merchants can make to enhance a product page, such as:
- Adding accurate and high-res images of the products
- Including or updating a sizing chart or guide
- Allowing customers to post pictures of their products in the reviews
- Adding a note in the product description that it’s recommended to “size up” or “size down”
Addressing these issues is essential for ecommerce merchants. Enhancing product pages can have a huge effect on building repeat shoppers and improving customer lifetime value.
4. Segment Customers Based on Your Returns Data
By harnessing the power of returns data, ecommerce businesses can segment customers based on their return behavior and preferences. A personalized marketing approach can maximize profits. It also strengthens the connection between your brand and customers, making them feel understood.
You can segment customers based on return behavior by sorting them into groups. Understanding these customer segments gives ecommerce merchants the ability to develop targeted strategies to address their specific needs and concerns.
Examples of these groups are “high-returners,” “occasional returners,” and “low-returners.” High-returners are customers who often return products. Occasional returners have a moderate return rate. Low returners rarely return items.
Knowing the frequency of product returns from a customer helps you to manage them better when they initiate a new RMA request. High returners may be suspected of fraudulent behavior. Low returners may be offered an exchange incentive to avoid a refund. This can help save the sale.
Another possible segmentation strategy would be to group customers based on return preferences which involves analyzing the reasons behind returns. For example, some customers may frequently return products due to sizing issues, while others may return items because of quality concerns.
By identifying these preferences, you can create personalized marketing campaigns that highlight solutions to these specific issues. This targeted approach increases the likelihood of customers finding products that meet their expectations, reducing returns and increasing profitability.
The same goes for segmenting customers based on demographic or psychographic factors. By analyzing return data alongside customer information, you can identify patterns and preferences among different demographic groups.
Certain age groups have higher return rates than others. This could be caused by their preferences or buying behaviors. This information enables you to customize your marketing materials, product suggestions, and promotions to appeal to particular segments. This will result in higher conversion rates, increased profit, and improved CLV.
Identify Trends in Customer Behavior
Analyzing return rates and reasons allow merchants to identify specific customer groups with unique characteristics and preferences. Businesses can gain a better understanding of customer behavior by segmenting them based on return data. With this understanding, they can create targeted strategies to meet the specific needs of their customers.
Analyzing return rates and reasons across different customer segments provides valuable insights. These insights can be used to develop deeper customer relationships and improve the Customer Lifetime Value (CLV).
Understanding the reasons behind returns can help inform marketing and advertising strategies. Identifying trends and patterns in customer behavior can lead to targeted campaigns. These campaigns can connect with specific segments and increase engagement and conversion rates.
By understanding customer trends and behavior patterns, businesses can provide a more personalized and tailored experience. This enhances customer loyalty, increases repeat purchases, and encourages customers to become brand advocates. All of these efforts will work towards improving CLV.
Create a Targeted Marketing Strategy
Once you have segmented your customer base using returns data, you can implement targeted marketing campaigns. For example, you can send personalized emails to high-returners, offering them exclusive discounts or incentives to encourage repeat purchases.
For low-returners, you can focus on building loyalty and retention by providing exceptional customer service and rewards programs. By customizing your marketing efforts based on customer segmentation analysis, you can maximize engagement and ultimately increase profitability.
Leveraging returns data to segment your customer base helps you personalize your marketing strategy, which will increase your CLV in turn. By sorting customers according to their return behavior, preferences, or demographic factors, merchants can increase brand loyalty.
This targeted marketing strategy increases customer satisfaction, reduces returns, and ultimately drives profitability for your business.
Include Personalized Recommendations to Increase Average Order Value
Merchants can gain useful knowledge from analyzing returns data. This enables them to classify customers by creating segments.
It also allows them to better understand customer behavior and preferences. Customer segmentation enables targeted marketing efforts and the delivery of personalized recommendations that resonate with each segment’s specific needs and preferences.
Understanding the different types of segmentation gives merchants the ability to customize their marketing strategies and personalized recommendations. This allows them to address the specific needs and concerns of each segment. These recommendations should include alternative or complementary items that fit their needs and preferences.
A customer who frequently returns clothing items due to sizing issues can be helped by the business. The business can recommend items in the correct size range. Alternatively, they can provide sizing guides to improve the accuracy of future purchases.
Implementing personalized recommendations based on returns data also enables businesses to upsell and cross-sell effectively. By strategically bundling or promoting these products, you can encourage customers to make additional purchases and increase their CLV.
Through returns data, ecommerce merchants can discover which products are often returned together or returned in combination with specific items. Armed with this knowledge, they can create bundled offers or promote complementary products to customers who are likely to be interested.
This increases the average order value. It also gives customers a more complete solution. This leads to increased customer satisfaction and loyalty. This personalized approach increases the chances of converting returns into new purchases, boosting sales and overall growth.
Use Returns Data to Grow Your Shopify Store
Leveraging returns data effectively presents a unique opportunity to grow your Shopify store. Shopify merchants can gain insight into product performance and customer preferences. This knowledge gives them a better understanding of how customers interact with their brands.
By analyzing the reasons for returns, you can identify product quality issues, sizing discrepancies, or other common pain points. This information can then be used to:
- Refine product offerings
- Improve quality control processes
- Source products that align with customer’s expectations
- Enhance customer retention marketing strategies and improve CLV
- Increase customer loyalty
All of these efforts will improve the overall business, resulting in long-term growth. Having this information can help you better meet customer expectations when they visit your Shopify store. This knowledge empowers merchants to create more personalized marketing strategies that deepen customer relationships, leading to an improved customer lifetime value (CLV).
Get started on using your returns data to improve your Shopify store by booking a demo with us today!